Bidirectional Relationship between Stock Market Decline and Liquidity: A Study of Emerged & Emerging Economies
Bibliographic record
Abstract
Purpose: This study intends to examine the nature & direction of relationship between stock market movements, particularly market decline, and its liquidity in 14 selected emerged and emerging economies (G8+5 and Pakistan) for January 2001 through December 2017 by applying Autoregressive Distributed Lag (ARDL) Bounds test and Granger-causality test. Trading value and turnover ratio are employed to measure market liquidity. Methodology: The study is conducted on a sample of 14 economies (G8 + 5 emerging economies, and Pakistan) for January 2001 through December 2017. Daily basis data for all variables is collected from data stream and Economic Indicator website. Market Liquidity is measured by trading value and turnover ratio Findings: Results of trading value Granger-causality test highlight the evidence of no causality in Germany & India. Bi-directional causality exists in Pakistan only. Uni-directional causality subsists only in Russia at 10% significance level from trading value to market return. However, from market return to trading value, results demonstrate the presence of uni-directional causality at 5% significance level for Brazil, Japan. Canada, China, France, Italy, UK, USA, South Africa and Mexico. Negative returns are used to represent the notion of market decline. Implications: Study summarizes the stock market movements of emerging and emerged countries which will be helpful for future researchers and policy makers in their projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".